Question

Which clustering algorithm can detect clusters of arbitrary shape and handle noise effectively ?

• A. K-Means
• B. Mean shift
• C. DBSCAN
• D. Agglomerating hierarchical clustering

Hint:

If you see "Arbitrary Shape" and "Noise" together in a clustering question, the answer is almost always DBSCAN.

Answer 1

The Correct Option is C

The choice of a clustering algorithm depends heavily on the distribution of the data and whether it contains outliers or "noise." 1. Limitations of K-Means: K-Means is a centroid-based algorithm that assumes clusters are spherical and of similar size. It struggles with clusters of arbitrary shapes (like "moons" or "donuts") and is highly sensitive to noise, as every point must be assigned to a cluster. 2. The DBSCAN Advantage: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) works by identifying "dense" regions of points.
• Arbitrary Shapes: Because it connects adjacent high-density areas, it can follow any shape or contour in the data.
• Noise Handling: It distinguishes between "Core," "Border," and "Noise" points. Points that do not belong to a dense region are explicitly labeled as noise/outliers rather than being forced into a cluster. 3. Conclusion: DBSCAN is specifically designed to handle spatial data where noise is present and clusters do not follow a simple geometric pattern.